arXiv subjects

Alan Wagner

Publications and source records attributed to Alan Wagner.

4 recordsLinked to original sources

arXiv2023

Modeling Evacuee Behavior for Robot-Guided Emergency Evacuation

This paper considers the problem of developing suitable behavior models of human evacuees during a robot-guided emergency evacuation. We describe our recent research developing behavior models of evacuees and potential future uses of these models. This paper considers how behavior models can contribute to the development and design of emergency evacuation simulations in order to improve social navigation during an evacuation.

cs.RO↗

arXiv2023

Near Real-Time Position Tracking for Robot-Guided Evacuation

During the evacuation of a building, the rapid and accurate tracking of human evacuees can be used by a guide robot to increase the effectiveness of the evacuation [1],[2]. This paper introduces a near real-time human position tracking solution tailored for evacuation robots. Using a pose detector, our system first identifies human joints in the camera frame in near real-time and then translates the position of these pixels into real-world coordinates via a simple calibration process. We run multiple trials of the system in action in an indoor lab environment and show that the system can achieve an accuracy of 0.55 meters when compared to ground truth. The system can also achieve an average of 3 frames per second (FPS) which was sufficient for our study on robot-guided human evacuation. The potential of our approach extends beyond mere tracking, paving the way for evacuee motion prediction, allowing the robot to proactively respond to human movements during an evacuation.

cs.RO↗

arXiv2020

Cognitively-Inspired Model for Incremental Learning Using a Few Examples

Incremental learning attempts to develop a classifier which learns continuously from a stream of data segregated into different classes. Deep learning approaches suffer from catastrophic forgetting when learning classes incrementally, while most incremental learning approaches require a large amount of training data per class. We examine the problem of incremental learning using only a few training examples, referred to as Few-Shot Incremental Learning (FSIL). To solve this problem, we propose a novel approach inspired by the concept learning model of the hippocampus and the neocortex that represents each image class as centroids and does not suffer from catastrophic forgetting. We evaluate our approach on three class-incremental learning benchmarks: Caltech-101, CUBS-200-2011 and CIFAR-100 for incremental and few-shot incremental learning and show that our approach achieves state-of-the-art results in terms of classification accuracy over all learned classes.

cs.CV↗

arXiv2003

On the Complexity of Buffer Allocation in Message Passing Systems

Message passing programs commonly use buffers to avoid unnecessary synchronizations and to improve performance by overlapping communication with computation. Unfortunately, using buffers makes the program no longer portable, potentially unable to complete on systems without a sufficient number of buffers. Effective buffer use entails that the minimum number needed for a safe execution be allocated. We explore a variety of problems related to buffer allocation for safe and efficient execution of message passing programs. We show that determining the minimum number of buffers or verifying a buffer assignment are intractable problems. However, we give a polynomial time algorithm to determine the minimum number of buffers needed to allow for asynchronous execution. We extend these results to several different buffering schemes, which in some cases make the problems tractable.

cs.DC↗